Method for passive wireless channel estimation in radio frequency network and apparatus for same

ABSTRACT

A method, system and apparatus are provided for estimating at least one characteristic of a wireless communication channel between at least two passive backscattering radio frequency (RF) nodes, the method including measuring backscatter channel state information (BCSI) during communication between the at least two passive RF nodes; estimating, by at least one RF node of the at least two passive RF nodes, the at least one characteristic of the wireless communication channel based on the measured BCSI.

PRIORITY

This application is a Continuation Application of U.S. application Ser.No. 16/972,829, filed with the U.S. Patent and Trademark Office on Dec.7, 2020, which is a National Phase Entry of PCT InternationalApplication No. PCT/US2019/035542, filed with the U.S. Patent andTrademark Office on Jun. 5, 2019, which claims the benefit of U.S.Provisional Application No. 62/680,813, filed with the U.S. Patent andTrademark Office on Jun. 5, 2018, the entire content of which isincorporated herein by reference.

GOVERNMENT SUPPORT

This invention was made with government support under grant numbersCNS-1405740 and CNS-1763843, each awarded by the National ScienceFoundation. The government has certain rights in the invention.

BACKGROUND OF THE INVENTION 1. Field

The present invention relates generally to wireless communicationsbetween passive Radio Frequency (RF) devices.

2. Related Art

RFID (Radio Frequency IDentification) tags are generally classified asbeing an active tag or a passive tag. Active RFID tags have an activelypowered transceiver. Passive RFID tags are powered by harvested ambientenergy.

Passive tag-to-tag communication is a relatively new technology [1, 4].Electromagnetic models for such communication were addressed in [5], andthere have been various efforts to advance this technology. One effortis presented in [1], where commercial TV signals were exploited forexcitation, and where communication ranges of a fraction of a meter werereported. In an effort to extend the range of the tag-to-tag link, CDMAencoding has been proposed [3]. Another approach to increase thecommunication range in tag-to-tag networks was to build customizedmulti-hop network architectures and routing protocols [6].

Efforts to improve hardware for tags of tag-to-tag networks is on-going[7, 8], as is tracking of events with such networks [9]. The possibilityof using a network as a device-free activity recognition system has beenexplored [10], because tags for communication exploit multiphaseprobing, which amounts to reflecting incident RF signals duringbackscattering with different phases of the reflected signal.

The backscattering communication principle until recently has beenmostly limited to RFID systems [11, 12, 13, 14, 15] with a standard RFIDsystem including an RFID reader, a computationally powerful device withactive radio and an ability to cancel the emitting RF signal from thesignal being received by the reader. For tag-to-reader communication,the tag simply modulates its antenna reflection coefficient by switchingbetween two impedances that terminate the tag antenna circuit [11],which effectively modulates the reflected signal back to the reader. Theactive reader demodulates this signal by employing IQ demodulation andactive cancellation of the interfering carrier signal. However, thelarge scale applications of RFID systems have been mostly limited by theinfrastructure cost of RFID reader deployment.

However, drawbacks of conventional systems and methods include highinfrastructure cost and high energy cost of active radios for wirelesschannel estimation.

SUMMARY

To overcome shortcomings of conventional methods, components andsystems, provided herein are methods, systems and an apparatus forestimating characteristics of a wireless communication channel betweenat least two passive RF nodes, as well as the advantages describedherein.

An aspect of the present disclosure provides a method for estimating atleast one characteristic of a wireless communication channel between atleast two passive backscattering radio frequency (RF) nodes, the methodincluding measuring backscatter channel state information (BCSI) duringcommunication between the at least two passive RF nodes; estimating, byat least one RF node of the at least two passive RF nodes, the at leastone characteristic of the wireless communication channel based on themeasured BCSI.

Another aspect of the present disclosure provides a passive RF node thatincludes a backscatter modulator; and at least one processor configuredto measure BCSI during communication with at least one other passive RFnode, and estimate at least one characteristic of at least onecharacteristic of the communication with the at least one other passiveRF node based on the measured BCSI.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of certainembodiments of the present invention will be more apparent from thefollowing detailed description taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates a network of a plurality of passive tags in a livingspace in accordance with an embodiment of the present disclosure;

FIG. 2 illustrates direct and reflected signals in a backscatteringtag-to-tag link scenario with a person located in the vicinity of thetags in accordance with an embodiment of the present disclosure;

FIG. 3 illustrates an experimental system setup using passive RF tagsfor collection of the BCSI vector data for passive channel estimationand activity recognition in accordance with an embodiment of the presentdisclosure;

FIG. 4 is a circuit diagram of a passive tag-to-tag channel estimator inaccordance with an embodiment of the present disclosure;

FIG. 5 is a graph of backscatter amplitude for running as an activity inaccordance with an embodiment of the present disclosure;

FIG. 6 is a graph of backscatter amplitude for falling as an activity inaccordance with an embodiment of the present disclosure; and

FIG. 7 is a graph of backscatter amplitude for walking as an activity inaccordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION

The following detailed description of certain embodiments references theaccompanying drawings. In the description, explanation about relatedfunctions or constructions known in the art are omitted for clarity.

FIG. 1 illustrates a network comprising a plurality of passive RFID tagsin a living space. The plurality of passive RFID tags communicate witheach other without the presence of an external RFID reader.

The plurality of passive RFID tags 130 a-130 j tags communicate witheach other directly in response to an RF signal, e.g. signal output fromexcitation source 110, in the network environment to supportbackscattering. The plurality of passive RFID tags either use local RFexciters or ambient RF signals for back-scattering. The sink 120 servesto upload information captured by the network to the cloud.

The plurality of passive RFID tags 130 a-130 j form the network bytag-to-tag backscatter 160. Multiphase probing of tag-to-tag channel isperformed within an area of the network. The plurality of passive RFIDtags exploit multiphase probing to provide rich RF analytics about theenvironment in the network in which the plurality of passive RFID tags130 a-130 j operate. As described herein, the analytics identify atleast one invariant with regard to a number of variables, including thedeployment environment, human subjects, location within the deploymentenvironment, and different deployment locations.

Enabling tag-to-tag communication based on the backscattering principleeliminates the need for RFID reader in the system. The added complexityin the RF tag capable of the tag-to-tag communication lies on thereceiving side. According to the present disclosure, the receiving (Rx)tag resolves a low modulation index signal reflected by the transmitting(Tx) tag. A conventional RFID tag is able to resolve a signal from atransmitting RFID tag only on a distance that is a fraction of a meter[4, 5]. With integrated signal amplification after envelope detection onthe RF tag, the range of tag-to-tag link is extended to a few meters [7,3]. RF tags then form a network transforming a conventional centralizedsystem with an RFID reader to a distributed system. The tag-to-tagnetwork only requires the presence of an RF signal in the environment.The RF signal can be either an ambient signal from WiFi APs or TVtowers, or can originate from a dedicated exciter device that emitscontinuous wave (CW) signal with zero intelligence.

The multiphase probing of present disclosure explores a backscatterchannel by reflecting an incident RF signal with different changes inthe phase and defining a measure of the backscatter channel, i.e.,backscatter channel state information (BCSI) to enable the system formedby the plurality of passive RFID tags to recognize activities. The BCSIis composed of backscatter channel phase, backscatter amplitude, andchange in baseline excitation level. When acquired over time, thismeasure provides rich RF analytics that are used to extract varioustypes of information from the environment of the tags by at least one ofa method of signal processing and a method of machine learning.

Techniques that are mostly used for activity recognition of a personthat does not carry or wear any device, i.e., device-free, rely onanalysis of wireless channels that ingrain information on reflectionsfrom a person and other living beings and objects in the environment[16, 17, 18]. Passive RF tags cannot perform IQ demodulation in order toestimate tag-to-tag channels due to their limited power budgets. Tagshave to rely on passive envelope demodulation that only obtains theamplitude of the received signal.

The present disclosure provides method, apparatus and system to estimatetag-to-tag channel characteristics. FIG. 2 illustrates direct andreflected signals in a backscattering tag-to-tag link scenario with aperson located in the vicinity of the tags. In the present disclosure,the passive tags for which tag-to-tag channel characteristics areestimated are mounted to objects.

As shown in FIG. 2 , a dedicated exciter 210 is provided with atransmitting (Tx) tag 310 and a receiving (Rx) tag 410. For simplicityof illustrating the derivation, the Tx tag 310 switches between twostates, open circuit and reflection with phase ϕ.

First, when an antenna circuit of the Tx tag 310 is open, the Rx tag 410only receives the signal from the exciter 210, according to Equation(1):

ν_(R1)(t)=A _(E)(t)e ^(j(ωt+θ) ^(E) ^((t)))  (1)

where ν_(R1) is the signal received at the Rx tag 410 in state 1, A_(E)is the amplitude of the exciter-Rx channel, and Θ_(E) is the phase ofthe exciter-Rx channel. The amplitude A_(E) and phase Θ_(E) of theexciter-Rx channel are dependent on the reflections from theenvironment.

The impedance of the antenna circuit is then changed, such that the Txtag 310 reflects an incident RF signal with a phase ϕ. The signalreceived at the Rx tag 410 combines the reflected signal from the Tx tag310 and the direct path signal from exciter according to Equation (2):

ν_(R2)(t)=A _(E)(t)e ^(j(ωt+θ) ^(E) ^((t)+ϕ))  (1)

where A_(B) is the amplitude of the backscatter and Θ_(B) is the phaseof the exciter-Tx-Rx channel. The baseband signal obtained at the Rx tag410 is the difference between the output of the envelope detector in thetwo states.

When the amplitude of the backscatter signal A_(B) is much smaller thanthe amplitude of the excitation signal A_(E), the difference between thetwo amplitudes simplifies to Equation (3):

$\begin{matrix}\begin{matrix}{{\Delta{v_{R}(t)}} = {{v_{R2}^{amp}(t)} - {v_{R1}^{amp}(t)}}} \\{\approx {A_{B}{{\cos\left( {\phi + {\theta_{B}(t)} - {\theta_{E}(t)}} \right)}.}}}\end{matrix} & (3)\end{matrix}$

The backscatter channel phase is Θ_(BC)(t)=Θ_(B)(t)−Θ_(E)(t).

To estimate the backscatter tag-to-tag channel, an estimation of theamplitude and phase A_(B) and Θ_(BC)(t) is performed. As the tags cannotdirectly measure these channel parameters, the channel parameters areexploited in Equation (4):

Δν_(R) =A _(B) cos(ϕ+θ_(BC)),  (4)

the phase ϕ is deterministic and is set by the Tx tag.

If the modulator of the Tx tag 310 varies the phase ϕ, the amplitude andphase of the backscatter signal is obtained using Equation (5):

$\begin{matrix}{{\theta_{BC} = {{\frac{\pi}{2} - \phi}❘_{{\Delta v_{R}} = 0}}},{A_{B} = {{\Delta v_{R}}❘_{\phi = {- \theta_{BC}}}.}}} & (5)\end{matrix}$

The modulator of the Tx tag operates in a plurality of states, with aset of discrete phases ϕ₁, ϕ₂, . . . , ϕ_(N), where N is the number oftotal states at which the modulator of the Tx tag backscatters. Thediscrete reflection phases ϕ₁ to ϕ_(N) are chosen to uniformly cover therange from 0 to π. The phase Θ_(BC) is estimated based on the value of ϕthat results in Δν_(R) being equal to zero. With a discrete number ofstates, Θ_(BC) is estimated from a weighted interpolation of two phasesadjacent to zero-crossing of Δν_(R). The amplitude A B is obtained byweighted interpolation of Ave between the same two phases, and thecoefficients of this interpolation will be the same as those used in theestimation of Θ_(BC). The number of phases N depends on the requiredresolution of the estimation of A_(B) and Θ_(BC), the signal-to-noiseratio (SNR) of the received baseband signal and the data rate of thetag-to-tag link.

For RF analytics, since the human body and other objects reflectwireless signals, any activity in the vicinity of the tags alters thewireless channels around them in specific ways. Hence, by using thecollated channel measurements received from over the tag network, thesystem infers analytic information about the environment, includinghumans and objects occupying the environment. The analytic informationabout the environment can also detect changes in spacing betweencomponents of a structure, such as changes in stanchions of a bridge,etc.

The dynamics of the exciter-Rx channel are not measured using theabove-described techniques since control of the phase of the signalemitted by the exciter is not directly controlled. Rather, recording thechanges in the excitation level A_(E) provides valuable supplementaryinformation about this channel, and the backscatter channel stateinformation (BCSI) is measured, based on the following three quantities:(1) backscatter channel phase Θ_(BC), (2) backscatter amplitude A B, and(3) change in excitation amplitude between two sampling intervalsΔA_(E). The BCSI vector recorded for a specific activity in anenvironment will have similar signature to the same activity performedin a different environment, as well as activity performed by a differentperson.

The BCSI vector serves as a feature vector which forms the basis ofactivity recognition. Once activity is detected in the presence of atleast two tags, the Tx tag enters a multi-phase probing (MPP)backscatter, in which, in a single MPP cycle, the backscatters has adiscrete reflection phase ϕ₁ to ϕ_(N). For each probing cycle, the Rxtag computes the BCSI vector for that cycle, h(t). During the activity,the BCSI vector is sampled, where the sampling rate is sufficientlyhigher than the frequency/speed of the activities. The determination ofthe sampling rate is also driven by the energy budget of the Rx tagwhich limits the backscatter data rate and the number of discretereflection phases. The sampled BCSI vector carries the distinctivesignature of a specific event and is then used for classification.

Invariance of RF analytics provides a basis for activity recognition.The BCSI measure is used for activity recognition with similar analyticssince the performance of a system is agnostic to the environment withinwhich the system is deployed. As set forth above, the BCSI vectorcontains the backscatter channel phase, backscatter channel amplitude,and the change in baseline excitation level. This vector is denoted byEquation (6):

h(t)=[Θ_(BC) A _(B) ΔA _(E)]  (6)

To perform activity recognition, the Tx tag sends out the MPP signalcontinuously for a predetermined number of cycles. For each cycle t, theRx tag computes the BCSI vector h(t). These continuous BCSI samples arewirelessly conveyed and analyzed to detect movement in the network, andindividual components of the BCSI are parsed for dynamic variationpatterns. The dynamic variation patterns in each individual componentjointly form an event signature which classifies the detected event. Forexample, the detected event can be movement of a person in the network,movement of a limb of the person in the network, movement of an objectin the network, and movement of a wall or other structure defining anetwork boundary.

The passive tags that form the network can be affixed to multiplelocations in the network. Detection of movement via BCSI analysis of awall or other structure that defines a network boundary is used toidentify unwanted structural changes, such as deflection of a wall,floor or ceiling in a building, or identification of movement of asupporting member, e.g., a bridge stanchion.

All analytics and event recognition are performed based on the dynamicvariation patterns in the BCSI vector components, not absolute valuethereof, thereby resulting in the invariance properties of the systemthat enhance robustness for use in practical situations. For example,invariance with regard to changes in the deployment environment, e.g.,static objects and clutter, does not require retraining of the system.Also, invariance with regard to human subjects allows event recognitionperformance to remain unchanged for differences in physical size andshape of the subject compared to the subject used for training thesystem. Further, since a dense deployment of tags is used and tag-to-taglinks are short range, the system can recognize events in all areaswithin the deployment zone given sufficient coverage of tags, therebyproviding invariance with regard to location within the deploymentenvironment. After system deployment and training, the system can bedeployed in a same constellation. Despite being in a totally differentenvironment, the system will perform identically without re-training.

FIG. 3 illustrates an experimental system setup using a pluralitypassive RF tags, i.e., nodes 630, 640 a, 640 b, 640 c and 640 d,arranged in a room of nine square meters for collection of the BCSIvector data for passive channel estimation and activity recognition. Asdescribed above, the exciter 610 can be replaced by ambient energy.

Each passive RF tag 630, 640 a, 640 b, 640 c and 640 d includes a singledipole antenna on a separate printed circuit board (PCB) and usesdiscrete component architectures for modulator and demodulatorimplementation for tag-to-tag communication. The modulator designincludes an RF switch which accommodates ten different reflectionphases. The demodulator consists of a passive envelope detector followedby a low-pass filter. The control is implemented on a low-powermicrocontroller, e.g., Texas Instruments TI MSP430. For measurement ofBCSI, the envelope detector output is connected to a PCB withhigh-resolution 16-bit 80 kbps ADC that enables data logging of theenvelope signal and off-line computation of the BCSI vector. The exciteris implemented using a software radio BladeRF and open source software[20]. The exciter emits a CW signal at 915 MHz. The BladeRF is connectedto a 9 dBi circularly polarized antenna [21].

FIG. 3 illustrates specified locations 650 a-d in the room surrounded bythe passive RF tag 630, 640 a, 640 b, 640 c and 640 d. Training andtesting samples were collected for nine participants that performed tendifferent daily activities in a lab setting. The activities were groupedinto eight classes, i.e., brushing, falling, running, no activity(person is either sitting or standing still), sitting down from standingposition, standing up from seating position, walking, and waving (eitherin sitting or standing position). The exciter power was set at 15 dBmand the transmitter with backscatter modulator of passive RF tag 630transmitted at different ten phases. The sampling time of the collectionof BCSI information was 50 ms and data was recorded for 2.5 seconds fromthe start of the activity. Each subject repeated each activity fivetimes in each of the four specified locations 650 a-d.

To estimate A_(B) and Θ_(BC), each activity was captured in a durationof 2.5 seconds using fifty transmissions. For each transmission,observations were obtained of amplitudes for a set of fixed phases, fromwhich a sinusoid function that is characterized by its phase andamplitude was estimated with standard signal processing techniques.

For invariance, each activity experiment was encoded by the dynamics ofthe BCSI vector. The encoded information not only captures signatures ofdifferent activities, but also is invariant with regard to, location,changes in deployment environment and human subject. To better visualizethe similarities, a comparison of only the dynamics of A_(B) isprovided, since the similarities in the dynamics of Θ_(BC) and ΔA_(E)need re-scaling, reversing and shifting.

FIG. 4 is a circuit diagram of a passive tag-to-tag channel estimator inaccordance with an embodiment of the present disclosure. As shown inFIG. 4 , the channel estimator includes an antenna, a transmitter forvoltage boosting that includes a backscatter modulator, matching andwireless communications, an envelope detector, an amplifier, the SARADC, and a logic circuit.

FIG. 5 is a graph of backscatter amplitude for running as an activity.FIG. 6 is a graph of backscatter amplitude for falling as an activity.FIG. 7 is a graph of backscatter amplitude for walking as an activity.To demonstrate invariance with regard to location, the dynamics of A_(B)from two BCSI vectors is shown.

In FIG. 5 , the two BCSI vectors were obtained from Rx tag 640 d,corresponding to a human subject performing the activity of running attwo different locations, at location 650 b and location 650 c,respectively. The similarity of the waveforms captured by the Rx tags atthe two different locations are clearly very similar.

Invariance with regard to changes in the deployment environment wasshown by adopting BCSI vectors from two different Rx tags, 640 a and tag640 d, that correspond to the same subject that performed the activityof falling at two different locations, respectively. The dynamicpatterns of the channel amplitude are shown in FIG. 6 . Again, thesimilarity of the patterns is apparent.

Invariance with regard to a human subject or movable object isdemonstrated in FIG. 7 , regarding an activity of walking performed atthe same location. A comparison of the amplitude from two BCSI vectorsobtained from a same Rx tag, one for subject 1 and the other for subject2. As shown in FIG. 7 , the two waveforms are very similar. Walking isan activity similar to running, albeit at a slower pace, and comparisonof FIG. 5 , where the illustrating running as the activity, and FIG. 7 ,shows waveforms of similar amplitude.

Accordingly, a method for estimating characteristics of a wirelesscommunication channel between at least two passive backscattering RFtags, i.e., nodes, is provided that includes measuring BCSI duringcommunication between the at least two passive RF nodes using thewireless communication channel; aggregating, by at least one RF node ofthe at least two passive RF nodes, the measured BCSI; and analyzing, bythe at least one RF node, the aggregated BSCI to detect at least oneactivity of a plurality of activities.

The BCSI is a feature vector h(t)=[Θ_(BC) A_(B) ΔA_(E)], with Θ_(BC),A_(B), and ΔA_(E) being a backscatter channel phase, a backscatteramplitude, and a change in excitation amplitude between samplingintervals, respectively. The backscatter channel phase isΘ_(BC)(t)=Θ_(B)(t) −Θ_(E)(t), with Θ_(B) and Θ_(E) being phases of anexciter transmitter to receiver channel, and the modulator of atransmitter tag varies a phase ϕ to obtain amplitude and phase of abackscatter signal according to Equation (5), above.

In response to detecting an activity of the plurality of activities, atransmit (Tx) node of the plurality of nodes transmits a multi-phaseprobing (MPP) backscatter signal for a plurality of cycles. A receiving(Rx) node of the plurality of nodes receives the MPP backscatter signal,and a BCSI vector is computed based on the received MPP backscattersignal. Also, components of the computed BCSI vector are parsed, dynamicvariation patterns of the parsed components of the BCSI vector areidentified, and a signature of an event based on the identified dynamicvariation patterns is detected, with the detected signature of the eventbeing invariant of an environment of the at least two passive RF nodes.

Provided are a passive RF node that includes a transmitter and at leastone processor configured to measure backscatter channel stateinformation (BCSI) during wireless communication with at least one otherpassive RF node, aggregate the measured BCSI, and analyze the aggregatedBSCI to detect at least one activity of a plurality of activities. TheBCSI is a feature vector comprising a backscatter channel phase, abackscatter amplitude, and a change in excitation amplitude betweensampling intervals. The backscatter channel phase is a differencebetween phases of an exciter transmitter and phases of a receiverchannel. In the passive RF node, a modulator of the transmitter varies aphase ϕ to obtain amplitude and phase of a backscatter signal accordingto Equation (5), above.

In response to detecting an activity of the plurality of activities, theat least one processor transmits a MPP backscatter signal for aplurality of cycles and the at least one other RF node of the pluralityof nodes receives the MPP backscatter signal, and a BCSI vector iscomputed based on the received MPP backscatter signal. Also, the atleast one processor parses components of the computed BCSI vector,identifies dynamic variation patterns of the parsed components of theBCSI vector, and detect a signature of an event based on the identifieddynamic variation patterns. The detected signature of the event isinvariant of an environment of the at least two passive RF nodes and arate of the aggregating of the measured BCSI is based on a predefinedenergy budget that limits a backscatter data rate and a number ofdiscrete reflection phases.

While the present disclosure has been shown and described with referenceto certain aspects thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the present disclosure,as defined by the appended claims and equivalents thereof.

REFERENCES

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What is claimed is:
 1. A method for estimating at least onecharacteristic of a wireless communication channel between at least twopassive backscattering radio frequency (RF) nodes, the methodcomprising: measuring backscatter channel state information (BCSI)during communication between the at least two passive RF nodes;estimating, by at least one RF node of the at least two passive RFnodes, the at least one characteristic of the wireless communicationchannel based on the measured BCSI.
 2. The method of claim 1, whereinthe BCSI is a feature vector h(t)=[Θ_(BC) A_(B) ΔA_(E)], with Θ_(BC),A_(B), and ΔA_(E) being a backscatter channel phase, a backscatteramplitude, and a change in excitation amplitude between samplingintervals, respectively.
 3. The method of claim 2, wherein thebackscatter channel phase is Θ_(BC)(t)=Θ_(B)(t)−Θ_(E)(t), and whereinΘ_(B) and Θ_(E) are phases of an exciter-transmitter-receiver channeland an exciter-transmitter channel, respectively.
 4. The method of claim3, wherein a backscatter modulator of a transmitter tag varies abackscatter signal phase ϕ to obtain amplitude and a phase of abackscatter signal at a receiver tag according to:${\theta_{BC} = {{\frac{\pi}{2} - \phi}❘_{{\Delta v_{R}} = 0}}},{A_{B} = {{\Delta v_{R}}❘_{\phi = {- \theta_{BC}}}}},.$5. The method of claim 1, wherein, in response to detecting an activityof the plurality of activities, a transmit (Tx) node of the plurality ofnodes transmits a multi-phase probing (MPP) signal for a plurality ofcycles, and wherein the MPP signal comprises a packet of data symbols,with a backscatter channel phase of the symbols within the packet beingsystematically varied.
 6. The method of claim 5, wherein a receiving(Rx) node of the plurality of nodes receives the MPP signal, and a BCSIvector is computed based on the received MPP signal.
 7. The method ofclaim 6, further comprising: parsing components of the computed BCSIvector; identifying dynamic variation patterns of the parsed componentsof the BCSI vector; and detecting a signature of an event based on theidentified dynamic variation patterns.
 8. The method of claim 7, whereinthe detected signature of the event is invariant of an environment ofthe at least two passive RF nodes.
 9. The method of claim 1, wherein thecommunication between the at least two passive RF nodes is powered byelectromagnetic energy harvested by at least one of the at least twopassive RF nodes.
 10. The method of claim 9, wherein a rate ofaggregating is based on an energy budget of a receiving (Rx) node of theplurality of nodes.
 11. The method of claim 10, wherein the energybudget limits a backscatter data rate and a number of discretereflection phases.
 12. A passive radio frequency (RF) node, comprising:a backscatter modulator; and at least one processor configured to:measure backscatter channel state information (BCSI) duringcommunication with at least one other passive RF node, and estimate atleast one characteristic of at least one characteristic of thecommunication with the at least one other passive RF node based on themeasured BCSI.
 13. The passive RF node of claim 12, wherein the BCSI isa feature vector comprising a backscatter channel phase Θ_(BC), abackscatter amplitude A_(B), and a change in excitation amplitude ΔA_(E)between sampling intervals.
 14. The passive RF node of claim 13, whereinthe backscatter channel phase Θ_(BC)(t) is a difference between phasesof an exciter transmitter Θ_(B)(t) and phases of a receiver channelΘ_(E)(t).
 15. The passive RF node of claim 14, wherein a backscattermodulator varies a backscatter signal phase ϕ to obtain amplitude andphase of a backscatter signal at the receiver according to:${\theta_{BC} = {{\frac{\pi}{2} - \phi}❘_{{\Delta v_{R}} = 0}}},{A_{B} = {{\Delta v_{R}}❘_{\phi = {- \theta_{BC}}}}},.$16. The passive RF node of claim 12, wherein, in response to detectingan activity of the plurality of activities, the at least one processoris further configured to transmit a multi-phase probing (MPP) signal fora plurality of cycles.
 17. The passive RF node of claim 16, wherein theat least one other RF node of the plurality of nodes receives the MPPsignal, and a BCSI vector is computed based on the received MPP signal.18. The passive RF node of claim 17, wherein the at least one processoris further configured to: parse components of the computed BCSI vector;identify dynamic variation patterns of the parsed components of the BCSIvector; and detect a signature of an event based on the identifieddynamic variation patterns.
 19. The passive RF node of claim 18, whereinthe detected signature of the event is invariant of an environment ofthe at least two passive RF nodes.
 20. The passive RF node of claim 12,wherein a rate of aggregating of the measured BCSI is based on apredefined energy budget that limits a backscatter data rate and anumber of discrete reflection phases.